U.S. patent application number 14/609869 was filed with the patent office on 2015-08-06 for generating vector representations of documents.
The applicant listed for this patent is Google Inc.. Invention is credited to Quoc V. Le.
Application Number | 20150220833 14/609869 |
Document ID | / |
Family ID | 52478097 |
Filed Date | 2015-08-06 |
United States Patent
Application |
20150220833 |
Kind Code |
A1 |
Le; Quoc V. |
August 6, 2015 |
GENERATING VECTOR REPRESENTATIONS OF DOCUMENTS
Abstract
Methods, systems, and apparatus, including computer programs
encoded on computer storage media, for generating document vector
representations. One of the methods includes obtaining a new
document; and determining a vector representation for the new
document using a trained neural network system, wherein the trained
neural network system has been trained to receive an input document
and a sequence of words from the input document and to generate a
respective word score for each word in a set of words, wherein each
of the respective word scores represents a predicted likelihood
that the corresponding word follows a last word in the sequence in
the input document, and wherein determining the vector
representation for the new document using the trained neural
network system comprises iteratively providing each of the
plurality of sequences of words to the trained neural network
system to determine the vector representation for the new document
using gradient descent.
Inventors: |
Le; Quoc V.; (Mountain View,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Google Inc. |
Mountain View |
CA |
US |
|
|
Family ID: |
52478097 |
Appl. No.: |
14/609869 |
Filed: |
January 30, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61934674 |
Jan 31, 2014 |
|
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|
Current U.S.
Class: |
706/16 |
Current CPC
Class: |
G06F 16/583 20190101;
G06N 3/08 20130101; G06N 3/084 20130101; G06N 3/04 20130101; G06F
40/284 20200101 |
International
Class: |
G06N 3/08 20060101
G06N003/08; G06N 3/04 20060101 G06N003/04 |
Claims
1. A method comprising: obtaining a new document, wherein the new
document includes a plurality of sequences of words, and, for each
sequence of words, a word that follows a last word in the sequence
of words in the new document; and determining a vector
representation for the new document using a trained neural network
system, wherein the trained neural network system has been trained
to receive an input document and a sequence of words from the input
document and to generate a respective word score for each word in a
pre-determined set of words, wherein each of the respective word
scores represents a predicted likelihood that the corresponding
word follows a last word in the sequence in the input document, and
wherein determining the vector representation for the new document
using the trained neural network system comprises iteratively
providing each of the plurality of sequences of words to the
trained neural network system to determine the vector
representation for the new document using gradient descent.
2. The method of claim 1, wherein the trained neural network system
comprises an embedding layer configured to map the input document
and each word in the sequence of words from the input document to
respective vector representations, a combining layer configured to
combine the vector representations into a combined representation,
and a classifier layer configured to generate the word scores using
the combined representation.
3. The method of claim 2, wherein the embedding layer maps the
words in the sequence of words to vector representations in
accordance with a first set of parameters, and wherein the
classifier layer generates the word scores from the combined
representation in accordance with a second set of parameters.
4. The method of claim 3, wherein the values of the first set of
parameters and the values of the second set of parameters are fixed
from training the neural network system to generate the word
scores.
5. The method of claim 2, wherein determining the vector
representation for the new document using the trained neural
network system comprises performing a respective iteration of
gradient descent for each of the plurality of sequences of words to
adjust the vector representation of the new document from a
previous iteration of gradient descent.
6. The method of claim 5, wherein the performing the respective
iteration of gradient descent for each of the plurality of
sequences comprises: mapping each of the words in the sequence to a
vector representation using the embedding layer; combining the
vector representation for the words in the sequence with the vector
representation for the new document from the previous iteration to
generate a combined representation; generating word scores from the
combined representation; computing a gradient using the word scores
and the word that follows the sequence in the new document; and
adjusting the vector representation for the new document from the
previous iteration using the gradient.
7. The method of claim 2, wherein the combining layer is configured
to concatenate the vector representations of the words in the
sequence with the vector representation of the input document.
8. The method of claim 2, wherein the combining layer is configured
to average the vector representations of the words in the sequence
and the vector representation of the input document.
9. The method of claim 1, wherein each of the plurality of
sequences of words contains a fixed number of words.
10. A method comprising: obtaining a plurality of training
documents, wherein each document in the plurality of training
documents includes a plurality of training sequences of words, and,
for each sequence of words, a word that follows a last word in the
training sequence of words in the training document; and training a
neural network system on each of the training documents using
gradient descent and backpropagation, wherein the neural network
system is configured to receive data identifying an input document
and an input sequence of words from the input document and to
generate a respective word score for each word in a pre-determined
set of words, wherein each of the respective word scores represents
a predicted likelihood that the corresponding word follows a last
word in the sequence of words in the input document, and wherein
training the neural network system on each of the training
documents comprises, for each training document, performing a
respective iteration of gradient descent for each sequence of words
in the training document.
11. The method of claim 10, wherein the neural network system
comprises an embedding layer configured to map the input document
and each word in the sequence of words from the input document to
respective vector representations, a combining layer configured to
combine the vector representations into a combined representation,
and a classifier layer configured to generate the word scores using
the combined representation.
12. The method of claim 11, wherein the embedding layer maps the
words in the sequence of words to vector representations in
accordance with a first set of parameters, and wherein the
classifier layer generates the word scores from the combined
representation in accordance with a second set of parameters.
13. The method of claim 12, wherein performing the respective
iteration of gradient descent for each of the plurality of
sequences in the training document comprises: mapping each of the
words in the sequence to a vector representation using the
embedding layer; mapping the data identifying the training document
to a vector representation using the embedding layer; combining the
vector representation for the words in the sequence with the vector
representation for the training document from the previous
iteration to generate a combined representation; generating word
scores from the combined representation; computing a gradient using
the word scores and the word that follows the sequence in the new
document; and adjusting values of the second set of parameters
using the gradient.
14. The method of claim 13, wherein performing the respective
iteration of gradient descent further comprises adjusting values of
the first set of parameters using backpropagation.
15. The method of claim 10, wherein the combining layer is
configured to concatenate the vector representations of the words
in the sequence with the vector representation of the input
document.
16. The method of claim 10, wherein the combining layer is
configured to average the vector representations of the words in
the sequence and the vector representation of the input
document.
17. The method of claim 10, wherein each of the plurality of
sequences of words contains a fixed number of words.
18. A system comprising one or more computers and one or more
storage devices storing instructions that when executed by the one
or more computers cause the one or more computers to perform
operations comprising: obtaining a new document, wherein the new
document includes a plurality of sequences of words, and, for each
sequence of words, a word that follows a last word in the sequence
of words in the new document; and determining a vector
representation for the new document using a trained neural network
system, wherein the trained neural network system has been trained
to receive an input document and a sequence of words from the input
document and to generate a respective word score for each word in a
pre-determined set of words, wherein each of the respective word
scores represents a predicted likelihood that the corresponding
word follows a last word in the sequence in the input document, and
wherein determining the vector representation for the new document
using the trained neural network system comprises iteratively
providing each of the plurality of sequences of words to the
trained neural network system to determine the vector
representation for the new document using gradient descent.
19. The system of claim 18, wherein the trained neural network
system comprises an embedding layer configured to map the input
document and each word in the sequence of words from the input
document to respective vector representations, a combining layer
configured to combine the vector representations into a combined
representation, and a classifier layer configured to generate the
word scores using the combined representation.
20. The system of claim 19, wherein determining the vector
representation for the new document using the trained neural
network system comprises performing a respective iteration of
gradient descent for each of the plurality of sequences of words to
adjust the vector representation of the new document from a
previous iteration of gradient descent.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to U.S. Provisional
Application No. 61/934,674, filed on Jan. 31, 2014. The disclosure
of the prior application is considered part of and is incorporated
by reference in the disclosure of this application.
BACKGROUND
[0002] This specification relates to text classification using data
processing systems.
[0003] Text classification systems can classify pieces of
electronic text, e.g., electronic documents. For example, text
classification systems can classify a piece of text as relating to
one or more of a set of predetermined topics. Some text
classification systems receive as input features of the piece of
text and use the features to generate the classification for the
piece of text.
[0004] Neural networks are machine learning models that employ one
or more layers of models to generate an output, e.g., a
classification, for a received input. Some neural networks include
one or more hidden layers in addition to an output layer. The
output of each hidden layer is used as input to the next layer in
the network, i.e., the next hidden layer or the output layer of the
network. Each layer of the network generates an output from a
received input in accordance with current values of a respective
set of parameters.
SUMMARY
[0005] In general, one innovative aspect of the subject matter
described in this specification can be embodied in methods that
include the actions of obtaining a new document, wherein the new
document includes a plurality of sequences of words, and, for each
sequence of words, a word that follows a last word in the sequence
of words in the new document; and determining a vector
representation for the new document using a trained neural network
system, wherein the trained neural network system has been trained
to receive an input document and a sequence of words from the input
document and to generate a respective word score for each word in a
pre-determined set of words, wherein each of the respective word
scores represents a predicted likelihood that the corresponding
word follows a last word in the sequence in the input document, and
wherein determining the vector representation for the new document
using the trained neural network system comprises iteratively
providing each of the plurality of sequences of words to the
trained neural network system to determine the vector
representation for the new document using gradient descent.
[0006] Other embodiments of this aspect include corresponding
computer systems, apparatus, and computer programs recorded on one
or more computer storage devices, each configured to perform the
actions of the methods.
[0007] A system of one or more computers can be configured to
perform particular operations or actions by virtue of having
software, firmware, hardware, or a combination of them installed on
the system that in operation causes or cause the system to perform
the actions. One or more computer programs can be configured to
perform particular operations or actions by virtue of including
instructions that, when executed by data processing apparatus,
cause the apparatus to perform the actions.
[0008] These and other aspects can optionally include one or more
of the following features. The trained neural network system can
include an embedding layer configured to map the input document and
each word in the sequence of words from the input document to
respective vector representations, a combining layer configured to
combine the vector representations into a combined representation,
and a classifier layer configured to generate the word scores using
the combined representation. The embedding layer can map the words
in the sequence of words to vector representations in accordance
with a first set of parameters, and the classifier layer can
generate the word scores from the combined representation in
accordance with a second set of parameters. The values of the first
set of parameters and the values of the second set of parameters
may be fixed from training the neural network system to generate
the word scores.
[0009] Determining the vector representation for the new document
using the trained neural network system can include performing a
respective iteration of gradient descent for each of the plurality
of sequences of words to adjust the vector representation of the
new document from a previous iteration of gradient descent.
Performing the respective iteration of gradient descent for each of
the plurality of sequences can include: mapping each of the words
in the sequence to a vector representation using the embedding
layer; combining the vector representation for the words in the
sequence with the vector representation for the new document from
the previous iteration to generate a combined representation;
generating word scores from the combined representation; computing
a gradient using the word scores and the word that follows the
sequence in the new document; and adjusting the vector
representation for the new document from the previous iteration
using the gradient. The combining layer can be configured to
concatenate the vector representations of the words in the sequence
with the vector representation of the input document. The combining
layer can be configured to average the vector representations of
the words in the sequence and the vector representation of the
input document. Each of the plurality of sequences of words can
contain a fixed number of words.
[0010] In general, another innovative aspect of the subject matter
described in this specification can be embodied in methods that
include the actions of obtaining a plurality of training documents,
wherein each document in the plurality of training documents
includes a plurality of training sequences of words, and, for each
sequence of words, a word that follows a last word in the training
sequence of words in the training document; and training a neural
network system on each of the training documents using gradient
descent and backpropagation, wherein the neural network system is
configured to receive data identifying an input document and an
input sequence of words from the input document and to generate a
respective word score for each word in a pre-determined set of
words, wherein each of the respective word scores represents a
predicted likelihood that the corresponding word follows a last
word in the sequence of words in the input document, and wherein
training the neural network system on each of the training
documents comprises, for each training document, performing a
respective iteration of gradient descent for each sequence of words
in the training document.
[0011] Other embodiments of this aspect include corresponding
computer systems, apparatus, and computer programs recorded on one
or more computer storage devices, each configured to perform the
actions of the methods.
[0012] These and other aspects can optionally include one or more
of the following features. The neural network system can include an
embedding layer configured to map the input document and each word
in the sequence of words from the input document to respective
vector representations, a combining layer configured to combine the
vector representations into a combined representation, and a
classifier layer configured to generate the word scores using the
combined representation. The embedding layer can map the words in
the sequence of words to vector representations in accordance with
a first set of parameters, and the classifier layer can generate
the word scores from the combined representation in accordance with
a second set of parameters. Performing the respective iteration of
gradient descent for each of the plurality of sequences in the
training document can include: mapping each of the words in the
sequence to a vector representation using the embedding layer;
mapping the data identifying the training document to a vector
representation using the embedding layer; combining the vector
representation for the words in the sequence with the vector
representation for the training document from the previous
iteration to generate a combined representation; generating word
scores from the combined representation; computing a gradient using
the word scores and the word that follows the sequence in the new
document; and adjusting values of the second set of parameters
using the gradient. Performing the respective iteration of gradient
descent can further include adjusting values of the first set of
parameters using backpropagation. The combining layer can be
configured to concatenate the vector representations of the words
in the sequence with the vector representation of the input
document. The combining layer can be configured to average the
vector representations of the words in the sequence and the vector
representation of the input document. Each of the plurality of
sequences of words can contain a fixed number of words.
[0013] Particular embodiments of the subject matter described in
this specification can be implemented so as to realize one or more
of the following advantages. A vector representation of a document
that can be used as a feature of the document, e.g., by a text
classification system, can be effectively generated. A neural
network system can be trained to generate the document
representation using only unlabeled training documents. The vector
representations generated by the trained neural network system can
have several desirable properties. For example, documents that are
semantically similar can have document vector representations that
are closer together than the document vector representations for
two documents that do not include semantically similar content.
[0014] The details of one or more embodiments of the subject matter
of this specification are set forth in the accompanying drawings
and the description below. Other features, aspects, and advantages
of the subject matter will become apparent from the description,
the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 shows an example vector representation system.
[0016] FIG. 2 is a flow diagram of an example process for
determining a document vector representation for a new
document.
[0017] FIG. 3 is a flow diagram of an example process for training
a neural network system on a sequence of words from a training
document.
[0018] FIG. 4 is a flow diagram of an example process for adjusting
the document vector representation for a new document.
[0019] Like reference numbers and designations in the various
drawings indicate like elements.
DETAILED DESCRIPTION
[0020] FIG. 1 shows an example vector representation system 100.
The vector representation system 100 is an example of a system
implemented as computer programs on one or more computers in one or
more locations, in which the systems, components, and techniques
described below can be implemented.
[0021] The vector representation system 100 generates word scores
for sequences of words from input documents, e.g., word scores 126
for a word sequence 106 from an input document 102. The word scores
126 for the word sequence 106 include a respective score for each
word in a pre-determined set of words, with the word score for a
given word representing the predicted likelihood that the word
follows the last word in the word sequence 106 in the input
document.
[0022] As part of generating word scores for sequences of words in
a given input document, the vector representation system 100
generates a document vector representation for the input document,
e.g., a document vector representation 120 for an input document
102. The document vector representation generated by the vector
representation system 100 is a vector representation of the
document. For example, the document vector representations may be
vectors of floating-point values or of quantized floating-point
values.
[0023] In particular, the vector representation system 100 includes
a neural network system 110 that, for a given word sequence in a
given input document, receives data identifying the input document
and the word sequence and processes the data identifying the input
document and the word sequence to generate the word scores for the
word sequence. For example, the vector representation system 100
can receive a document identifier 104 for the input document 102
and the word sequence 106 from the input document 102 and generate
the word scores 126 for the word sequence 106. The document
identifier may be, e.g., pre-assigned to the input document 102 or
be generated by the vector representation system 100 such that the
identifier uniquely identifies the input document 102.
[0024] The neural network system 110 includes an embedding layer
112, a combining layer 114, and a classifier layer 116. The
embedding layer 112 maps the data identifying the document to a
document vector representation, e.g., the document vector
representation 120 for the input document 102, in accordance with
current values of a set of document parameters. The embedding layer
112 also maps each word in the word sequence to a respective word
vector representation, e.g., word vector representations 122 for
the words in the word sequence 106, in accordance with current
values of a set of word parameters. Each word vector is a vector
representation of the corresponding word, e.g., a vector of
floating point or quantized floating point values.
[0025] The combining layer 114 receives the document vector
representation and the word vector representations and generates a
combined representation from the document vector representation and
the word vector representations, e.g., a combined representation
124 from the word vector representations 122 and the document
vector representation 120. Generating the combined representation
is described in more detail below with reference to FIGS. 3 and
4.
[0026] The classifier layer 116 receives the combined
representation and processes the combined representation to
generate the word scores for the word sequence from the input
document in accordance with current values of a set of classifier
parameters. For example, the classifier layer 116 may process the
combined representation 124 to generate the word scores 126 for the
word sequence 106.
[0027] The vector representation system 100 trains the neural
network system 110 on multiple word sequences from training
documents in order to determine trained values of the word
parameters and the classifier parameters. Training the neural
network system is described in more detail below with reference to
FIGS. 2 and 3. Once trained values of the word parameters and the
classifier parameters have been determined, the vector
representation system 100 can receive a new input document and
process sequences of words from the new input document using the
neural network system 110 to determine a document vector
representation for the new document. Generating a document vector
representation for a new document is described in more detail below
with reference to FIGS. 2 and 4.
[0028] Once the document vector representation for a given document
has been generated, the vector representation system 100 can
associate the document vector representation with the document in a
repository or provide the document representation to a separate
system for use for some immediate purpose. For example, the
document representation can be used as a feature of the input
document and can be provided as input to a conventional machine
learning system, e.g., a logistic regression system, a Support
Vector Machines (SVM) system, or a k-means system, that has been
configured to classify input documents, e.g., as relating to
particular topics. For example, the conventional machine learning
system may be configured to receive the document representation of
the input document and, optionally, other features of the input
document and generate a respective score for each of a set of
topics, with each score representing an estimated likelihood that
the document is about or relates to the corresponding topic.
[0029] FIG. 2 is a flow diagram of an example process 200 for
determining a document vector representation for a new document.
For convenience, the process 200 will be described as being
performed by a system of one or more computers located in one or
more locations. For example, a vector representation system, e.g.,
the vector representation system 100 of FIG. 1, appropriately
programmed, can perform the process 200.
[0030] The system trains a neural network system, e.g., the neural
network system 110 of FIG. 1, to generate word scores (step 202).
The neural network system is a system that includes an embedding
layer, a combining layer, and a classifier layer. The embedding
layer is configured to receive data identifying a document and a
sequence of words from the document, map the data identifying the
document to a document vector representation in accordance with
current values of a set of document parameters, and map each word
in the sequence of words to a respective word vector representation
in accordance with current values of a set of word parameters. The
combining layer is configured to combine the word vector
representations and the document vector representation to generate
a combined representation. The classifier layer is configured to
process the combined representation to generate a set of word
scores for the word sequence in accordance with current values of a
set of classifier parameters.
[0031] During the training, the system adjusts the values of the
word parameters and the classifier parameters to determine trained
values of the word parameters and the classifier parameters. In
particular, the system trains the neural network system on a set of
word sequences from a set of training documents. The training
documents may include, for example, one or more of: sentences,
paragraphs, collections of multiple paragraphs, search queries, or
other collections of multiple natural language words.
[0032] To adjust the values of the parameters of the neural network
system, the system performs an instance of a gradient descent
training procedure for each of the training sequence. In
particular, the system processes the sequence of words using the
neural network system to generate word scores for the sequence and
then adjusts the values of the parameters using the word scores and
the word that follows the last word in the sequence in the training
document, i.e., using gradient descent and backpropagation.
Adjusting the parameters of the neural network system using a word
sequence from a training document is described in more detail below
with reference to FIG. 3.
[0033] Because the system uses only the word scores for the word
sequence and the word that follows the last word in the word
sequence in the training document in order to adjust the values of
the parameters of the neural network system, the training documents
do not need to be labeled to be used in training the neural network
system. That is, the system can train the neural network system to
generate word scores using only sequences of words from unlabeled
training documents, i.e., documents that have not been classified
as relating to any particular topic or otherwise been processed by
a text classification system.
[0034] The system receives a new document (step 204). The new
document may be, for example, a sentence, a paragraph, a collection
of multiple paragraphs, a search query, or another collection of
multiple natural language words.
[0035] The system determines a document vector representation for
the new document using the trained neural network system (step
206). Generally, the system processes multiple word sequences from
the new document using the trained neural network system to
determine the document vector representation for the new document.
In particular, the system identifies multiple sequences of words
from the new document. In some implementations, each of the
sequences is a fixed length, i.e., includes the same fixed number
of words. For example, the system can apply a sliding window to the
new document to extract each possible sequence of a predetermined
fixed length from the new document.
[0036] The system can then process each of the extracted sequences
using the trained neural network system in order to iteratively
determine the document vector representation for the new document.
That is, the system adjusts the current representation of the new
document after each sequence from the new document is processed
through the trained neural network sequence to generate word scores
for the sequence. Adjusting a document vector representation for a
new document using a sequence from the new document is described in
more detail below with reference to FIG. 4.
[0037] FIG. 3 is a flow diagram of an example process 300 for
training a neural network system on a sequence of words from a
training document. For convenience, the process 300 will be
described as being performed by a system of one or more computers
located in one or more locations. For example, a vector
representation system, e.g., the vector representation system 100
of FIG. 1, appropriately programmed, can perform the process
300.
[0038] The system maps each of the words in the sequence to a
respective word vector representation using the embedding layer
(step 302). In particular, the system processes each word in the
sequence in accordance with current values of the word parameters
to determine a respective word vector representation for each of
the words in the sequence.
[0039] The system maps the training document to a document vector
representation using the embedding layer (step 304). In particular,
the system processes the data identifying the training document in
accordance with current values of the document parameters to
determine a document vector representation for the training
document.
[0040] The system generates a combined representation from the word
vector representations and the document vector representation using
the combining layer (step 306). In particular, the system processes
the word vector representations and the current document vector
representation using the combining layer to generate the combined
representation. For example, the combining layer may concatenate
the word vector representations and the current document vector
representation to generate the combined representation. As another
example, the combining layer may compute a measure of central
tendency, e.g., a mean, median, or other average, of the word
vector representations and the current document vector
representation to generate the combined representation.
[0041] The system generates word scores from the combined
representation using the classifier layer (step 308). In
particular, the system processes the combined representation using
the classifier layer and in accordance with current values of the
parameters of the classifier layer to generate a respective word
score for each word in the predetermined set of words.
[0042] The system computes a gradient using the word scores (step
310). That is, the system computes an error between the word scores
and the desired output for the sequence of words, i.e., a set of
word scores that indicates that the word that actually follows the
last word in the sequence in the new document is the next word in
the sequence, and then computes the gradient of the error.
[0043] The system adjusts current values of the parameters of the
neural network system using the gradient (step 312). In particular,
the system adjusts the current values of the parameters of the
classifier layer using the gradient of the error and then adjusts
the current values of the parameters of the embedding layer, i.e.,
the current values of the document parameters and the word
parameters, using backpropagation.
[0044] The system can perform the process 300 for each of multiple
training sequences from multiple training documents in order to
iteratively determine the trained values of the parameters of the
document. For example, for each iteration of the process 300, the
system can randomly select a training document and a fixed length
sequence of words from the training document. The system can then
perform iterations of the process 300 on sequences from training
documents until each possible sequence has been processed or until
other termination criteria for the training have been
satisfied.
[0045] FIG. 4 is a flow diagram of an example process 400 for
adjusting the document vector representation for a new document.
For convenience, the process 400 will be described as being
performed by a system of one or more computers located in one or
more locations. For example, a vector representation system, e.g.,
the vector representation system 100 of FIG. 1, appropriately
programmed, can perform the process 400.
[0046] The system receives a sequence of words from the new
document (step 402). For example, the sequence of words may be a
fixed length sequence of words that has been extracted from the new
document.
[0047] The system maps each of the words in the sequence to a
respective vector representation (step 404). That is, the system
processes each of the words in the sequence using the embedding
layer to map each word to a word vector representation in
accordance with trained values of the word parameters.
[0048] The system maps the new document to a document vector
representation (step 406). That is, the system processes data
identifying the new document using the embedding layer to map the
new document to a document vector representation in accordance with
current values of the document parameters.
[0049] The system generates a combined representation using the
combining layer (step 406). In particular, the system processes the
word vector representations and the document vector representation
using the combining layer to generate the combined representation.
For example, the combining layer may concatenate the word vector
representations and the current document vector representation to
generate the combined representation. As another example, the
combining layer may compute a measure of central tendency, e.g., a
mean, median, or other average, of the word vector representations
and the current document vector representation to generate the
combined representation.
[0050] The system generates word scores from the combined
representation using the classifier layer (step 408). In
particular, the system processes the combined representation using
the classifier layer and in accordance with the trained values of
the parameters of the classifier layer to generate a respective
word score for each word in the predetermined set of words.
[0051] The system computes a gradient using the word scores (step
410). That is, the system computes an error between the word scores
and the desired output for the sequence of words, i.e., a set of
word scores that indicates that the word that actually follows the
last word in the sequence in the new document is the next word in
the sequence, and then computes the gradient of the error.
[0052] The system adjusts the vector representation for the new
document using the gradient (step 412). That is, the system holds
the trained values of the parameters of the classifier layer and
the word parameters fixed and updates the current values of the
document parameters using backpropagation.
[0053] The system then uses the updated values of the document
parameters when computing the document vector representation for
the next sequence of words from the new document. Alternatively, if
the current sequence of words is the last sequence to be processed
from the new document, the system computes an adjusted document
vector representation of the new document using the updated values
of the document parameters and uses the adjusted document vector
representation as the document representation of the new
document.
[0054] Embodiments of the subject matter and the functional
operations described in this specification can be implemented in
digital electronic circuitry, in tangibly-embodied computer
software or firmware, in computer hardware, including the
structures disclosed in this specification and their structural
equivalents, or in combinations of one or more of them. Embodiments
of the subject matter described in this specification can be
implemented as one or more computer programs, i.e., one or more
modules of computer program instructions encoded on a tangible non
transitory program carrier for execution by, or to control the
operation of, data processing apparatus. Alternatively or in
addition, the program instructions can be encoded on an
artificially generated propagated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal, that is generated
to encode information for transmission to suitable receiver
apparatus for execution by a data processing apparatus. The
computer storage medium can be a machine-readable storage device, a
machine-readable storage substrate, a random or serial access
memory device, or a combination of one or more of them.
[0055] The term "data processing apparatus" encompasses all kinds
of apparatus, devices, and machines for processing data, including
by way of example a programmable processor, a computer, or multiple
processors or computers. The apparatus can include special purpose
logic circuitry, e.g., an FPGA (field programmable gate array) or
an ASIC (application specific integrated circuit). The apparatus
can also include, in addition to hardware, code that creates an
execution environment for the computer program in question, e.g.,
code that constitutes processor firmware, a protocol stack, a
database management system, an operating system, or a combination
of one or more of them.
[0056] A computer program (which may also be referred to or
described as a program, software, a software application, a module,
a software module, a script, or code) can be written in any form of
programming language, including compiled or interpreted languages,
or declarative or procedural languages, and it can be deployed in
any form, including as a stand-alone program or as a module,
component, subroutine, or other unit suitable for use in a
computing environment. A computer program may, but need not,
correspond to a file in a file system. A program can be stored in a
portion of a file that holds other programs or data, e.g., one or
more scripts stored in a markup language document, in a single file
dedicated to the program in question, or in multiple coordinated
files, e.g., files that store one or more modules, sub programs, or
portions of code. A computer program can be deployed to be executed
on one computer or on multiple computers that are located at one
site or distributed across multiple sites and interconnected by a
communication network.
[0057] The processes and logic flows described in this
specification can be performed by one or more programmable
computers executing one or more computer programs to perform
functions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry, e.g.,
an FPGA (field programmable gate array) or an ASIC (application
specific integrated circuit).
[0058] Computers suitable for the execution of a computer program
include, by way of example, can be based on general or special
purpose microprocessors or both, or any other kind of central
processing unit. Generally, a central processing unit will receive
instructions and data from a read only memory or a random access
memory or both. The essential elements of a computer are a central
processing unit for performing or executing instructions and one or
more memory devices for storing instructions and data. Generally, a
computer will also include, or be operatively coupled to receive
data from or transfer data to, or both, one or more mass storage
devices for storing data, e.g., magnetic, magneto optical disks, or
optical disks. However, a computer need not have such devices.
Moreover, a computer can be embedded in another device, e.g., a
mobile telephone, a personal digital assistant (PDA), a mobile
audio or video player, a game console, a Global Positioning System
(GPS) receiver, or a portable storage device, e.g., a universal
serial bus (USB) flash drive, to name just a few.
[0059] Computer readable media suitable for storing computer
program instructions and data include all forms of non-volatile
memory, media and memory devices, including by way of example
semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory
devices; magnetic disks, e.g., internal hard disks or removable
disks; magneto optical disks; and CD ROM and DVD-ROM disks. The
processor and the memory can be supplemented by, or incorporated
in, special purpose logic circuitry.
[0060] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, e.g., a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor, for displaying
information to the user and a keyboard and a pointing device, e.g.,
a mouse or a trackball, by which the user can provide input to the
computer. Other kinds of devices can be used to provide for
interaction with a user as well; for example, feedback provided to
the user can be any form of sensory feedback, e.g., visual
feedback, auditory feedback, or tactile feedback; and input from
the user can be received in any form, including acoustic, speech,
or tactile input. In addition, a computer can interact with a user
by sending documents to and receiving documents from a device that
is used by the user; for example, by sending web pages to a web
browser on a user's client device in response to requests received
from the web browser.
[0061] Embodiments of the subject matter described in this
specification can be implemented in a computing system that
includes a back end component, e.g., as a data server, or that
includes a middleware component, e.g., an application server, or
that includes a front end component, e.g., a client computer having
a graphical user interface or a Web browser through which a user
can interact with an implementation of the subject matter described
in this specification, or any combination of one or more such back
end, middleware, or front end components. The components of the
system can be interconnected by any form or medium of digital data
communication, e.g., a communication network. Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), e.g., the Internet.
[0062] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other.
[0063] While this specification contains many specific
implementation details, these should not be construed as
limitations on the scope of any invention or of what may be
claimed, but rather as descriptions of features that may be
specific to particular embodiments of particular inventions.
Certain features that are described in this specification in the
context of separate embodiments can also be implemented in
combination in a single embodiment. Conversely, various features
that are described in the context of a single embodiment can also
be implemented in multiple embodiments separately or in any
suitable subcombination. Moreover, although features may be
described above as acting in certain combinations and even
initially claimed as such, one or more features from a claimed
combination can in some cases be excised from the combination, and
the claimed combination may be directed to a subcombination or
variation of a subcombination.
[0064] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system modules and components in the
embodiments described above should not be understood as requiring
such separation in all embodiments, and it should be understood
that the described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0065] Particular embodiments of the subject matter have been
described. Other embodiments are within the scope of the following
claims. For example, the actions recited in the claims can be
performed in a different order and still achieve desirable results.
As one example, the processes depicted in the accompanying figures
do not necessarily require the particular order shown, or
sequential order, to achieve desirable results. In certain
implementations, multitasking and parallel processing may be
advantageous.
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